{"ID":2826851,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18537","arxiv_id":"2512.18537","title":"Systematic Benchmarking of SUMO Against Data-Driven Traffic Simulators","abstract":"This paper presents a systematic benchmarking of the model-based microscopic traffic simulator SUMO against state-of-the-art data-driven traffic simulators using large-scale real-world datasets. Using the Waymo Open Motion Dataset (WOMD) and the Waymo Open Sim Agents Challenge (WOSAC), we evaluate SUMO under both short-horizon (8s) and long-horizon (60s) closed-loop simulation settings. To enable scalable evaluation, we develop Waymo2SUMO, an automated pipeline that converts WOMD scenarios into SUMO simulations. On the WOSAC benchmark, SUMO achieves a realism meta metric of 0.653 while requiring fewer than 100 tunable parameters. Extended rollouts show that SUMO maintains low collision and offroad rates and exhibits stronger long-horizon stability than representative data-driven simulators. These results highlight complementary strengths of model-based and data-driven approaches for autonomous driving simulation and benchmarking.","short_abstract":"This paper presents a systematic benchmarking of the model-based microscopic traffic simulator SUMO against state-of-the-art data-driven traffic simulators using large-scale real-world datasets. Using the Waymo Open Motion Dataset (WOMD) and the Waymo Open Sim Agents Challenge (WOSAC), we evaluate SUMO under both short...","url_abs":"https://arxiv.org/abs/2512.18537","url_pdf":"https://arxiv.org/pdf/2512.18537v1","authors":"[\"Erdao Liang\"]","published":"2025-12-20T23:26:14Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}